Build gridded livestock dataset — build_gridded

Disaggregate country-level FAOSTAT livestock stocks and emissions to a 0.5-degree grid. Each species group uses a tailored spatial proxy:

Ruminants (cattle, buffalo, sheep/goats, equines): LUH2 managed pasture (pastr) plus rangeland (range), optionally weighted by a static manure-intensity reference (West et al. 2014).
Confined animals (pigs, poultry): LUH2 aggregate cropland, reflecting intensive farming co-location with crop production.
Range specialists (camels): LUH2 rangeland only.
Mixed (other animals): 50/50 blend of pasture and cropland.

For each country, year, and species group the function distributes the national total proportionally to cell-level proxy weights:

$$\text{cell} = \frac{w_i}{\sum_{j \in \text{country}} w_j} \times T_{\text{country}}$$

where $w_i$ is the proxy weight in cell $i$ (land-use hectares times optional reference-pattern intensity) and $T$ is the country total (heads or emissions).

Methodology

Livestock spatialization is not covered by LandInG (Ostberg et al. 2023), which focuses on crops only. The approach here extends the LandInG framework by using the same LUH2-based spatial proxies (pasture, rangeland, cropland) for livestock distribution.

Country-level data comes from build_primary_production() (stocks) and the faostat-emissions-livestock pin (CH4/N2O emissions), with predecessor redistribution and pre-1961 backfill already applied.

The Zenodo livestock density input (Heinke 2025, doi:10.5281/zenodo.14946695) provides an alternative calibrated LSU/ha reference for use with the glw_density parameter.

Data sources and references

Source	Use
FAOSTAT Production_Livestock (FAO 2024)	Country-level heads
FAOSTAT Emissions_livestock (FAO 2024)	Enteric CH4, manure CH4/N2O
LUH2 v2h (Hurtt et al. 2020)	Time-varying pasture + cropland
West et al. (2014)	Static manure-N intensity reference
GLW3 (Gilbert et al. 2018)	Species-specific density (optional)
Heinke (2025)	Calibrated LSU/ha density (optional)
IPCC 2006/2019	N-excretion rates, emission factors

Usage

build_gridded_livestock(
  livestock_data,
  gridded_pasture,
  gridded_cropland,
  country_grid,
  species_proxy = NULL,
  manure_pattern = NULL,
  glw_density = NULL,
  years = NULL
)

Arguments

livestock_data

A tibble with country-level livestock data. Required columns:

year: Integer year.
area_code: Country code (WHEP polities).
species_group: Livestock functional-type name (e.g. "cattle", "pigs", "poultry").
heads: Live animal count (number of head). Any additional numeric columns (e.g. enteric_ch4_kt, manure_ch4_kt, manure_n2o_kt, manure_n_mg) are distributed to the grid using the same proportional weights as heads.

gridded_pasture

A tibble with annual gridded pasture extent. Required columns:

lon, lat: Cell centre coordinates (0.5 degree).
year: Integer year.
pasture_ha: Managed pasture area in hectares (LUH2 pastr).
rangeland_ha: Rangeland area in hectares (LUH2 range).

gridded_cropland

A tibble with annual gridded cropland extent. Required columns:

lon, lat: Cell centre coordinates.
year: Integer year.
cropland_ha: Total cropland area in hectares.

country_grid

A tibble mapping grid cells to countries. Required columns:

lon, lat: Cell centre coordinates.
area_code: Country code. Optional columns:
cell_area_frac (or area_frac): Fraction of the physical cell belonging to this polity compartment. Defaults to 1.
polycell_id, cell_id: Stable compartment/cell identifiers preserved in outputs when present.
year or validity intervals (valid_from/valid_to, start_year/end_year, from_year/to_year) for historical, time-varying polity overlays.

species_proxy

A tibble mapping each species_group to its spatial proxy type: "pasture", "cropland", "rangeland", or "mixed". Required columns:

species_group: Group name (must match livestock_data).
spatial_proxy: One of "pasture", "cropland", "rangeland", or "mixed". If NULL, a default mapping is used (see Details).

manure_pattern

A tibble with static manure-intensity weights (e.g. from West et al. 2014). Optional. Expected columns:

lon, lat: Cell centre coordinates.
manure_intensity: Relative intensity (kg N per ha or similar). Values are used multiplicatively with the land-use proxy. If NULL, land-use weights are used alone.

glw_density

A tibble with species-specific gridded livestock density from GLW3 (Gilbert et al. 2018). Optional. Expected columns:

lon, lat: Cell centre coordinates.
species_group: Must match livestock_data.
density: Heads per cell (reference year ~2010). If provided, this replaces the LUH2-based proxy for the matching groups, while still being scaled by LUH2 time trends. If NULL, LUH2 proxies are used for all groups.

years

Integer vector of years to spatialize. If NULL (default), all years present in livestock_data are processed. When supplied, livestock_data, gridded_pasture, and gridded_cropland are filtered to this set before processing.

Value

A tibble with gridded livestock data. Columns:

lon, lat: Cell centre coordinates.
area_code: WHEP polity code for this cell compartment.
polycell_id, cell_id: Preserved when supplied in country_grid.
year: Integer year.
species_group: Livestock functional type.
heads: Allocated live animal count.
Any additional numeric columns from livestock_data (e.g. enteric_ch4_kt, manure_ch4_kt).

Examples

# Minimal example with toy data
livestock_data <- tibble::tribble(
  ~year, ~area_code, ~species_group, ~heads,
  2000L,         1L,       "cattle",   5000
)
gridded_pasture <- tibble::tribble(
  ~lon,  ~lat,  ~year, ~pasture_ha, ~rangeland_ha,
   0.25, 50.25, 2000L,         600,           200,
   0.75, 50.25, 2000L,         400,           100
)
gridded_cropland <- tibble::tribble(
  ~lon,  ~lat,  ~year, ~cropland_ha,
   0.25, 50.25, 2000L,          800,
   0.75, 50.25, 2000L,          500
)
country_grid <- tibble::tribble(
  ~lon,  ~lat, ~area_code,
   0.25, 50.25,         1L,
   0.75, 50.25,         1L
)
build_gridded_livestock(
  livestock_data, gridded_pasture, gridded_cropland, country_grid
)
#> ℹ Spatializing 1 groups over 1 years
#> # A tibble: 2 × 6
#>    year species_group area_code   lon   lat heads
#>   <int> <chr>             <int> <dbl> <dbl> <dbl>
#> 1  2000 cattle                1  0.25  50.2 3077.
#> 2  2000 cattle                1  0.75  50.2 1923.